Classification of signals by means of Genetic Programming

@Article{journals/soco/Fernandez-BlancoRGD13,
author = "Enrique Fernandez-Blanco and Daniel Rivero and
Marcos Gestal and Julian Dorado",
title = "Classification of signals by means of Genetic
Programming",
journal = "Soft Computing",
year = "2013",
number = "10",
volume = "17",
pages = "1929--1937",
keywords = "genetic algorithms, genetic programming, GP, Automatic
feature extraction Automatic classification Signal
processing",
bibdate = "2013-09-09",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/soco/soco17.html#Fernandez-BlancoRGD13",
URL = "http://dx.doi.org/10.1007/s00500-013-1036-4",
size = "9 pages",
abstract = "This paper describes a new technique for signal
classification by means of Genetic Programming (GP).
The novelty of this technique is that no prior
knowledge of the signals is needed to extract the
features. Instead of it, GP is able to extract the most
relevant features needed for classification. This
technique has been applied for the solution of a
well-known problem: the classification of EEG signals
in epileptic and healthy patients. In this problem,
signals obtained from EEG recordings must be correctly
classified into their corresponding class. The aim is
to show that the technique described here, with the
automatic extraction of features, can return better
results than the classical techniques based on manual
extraction of features. For this purpose, a final
comparison between the results obtained with this
technique and other results found in the literature
with the same database can be found. This comparison
shows how this technique can improve the ones found.",
}